Simple phasor-based deep neural network for fluorescence lifetime imaging microscopy.

ABSTRACT: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to probe the molecular environment of fluorophores. The analysis of FLIM images is usually performed with time consuming fitting methods. For accelerating this analysis, sophisticated deep learning architectures based on convolutional neural networks have been developed for restrained lifetime ranges but they require long training time. In this work, we present a simple neural network formed only with fully connected layers able to analyze fluorescence lifetime images. It is based on the reduction of high dimensional fluorescence intensity temporal decays into four parameters which are the phasor coordinates, the mean and amplitude-weighted lifetimes. This network called Phasor-Net has been applied for a time domain FL

SUBMITTER: Heliot L 

PROVIDER: S-EPMC8668934 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature